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from langchain_community.vectorstores import FAISS |
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from langchain_community.embeddings.huggingface import HuggingFaceBgeEmbeddings |
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import firebase_admin |
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from firebase_admin import credentials, storage |
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import json, os, dotenv |
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import pandas as pd |
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from dotenv import load_dotenv |
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load_dotenv() |
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try: |
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os.environ["FIREBASE_CREDENTIAL"] = dotenv.get_key(dotenv.find_dotenv(), "FIREBASE_CREDENTIAL") |
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if os.environ.get["FIREBASE_CREDENTIAL"] == None: |
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raise TypeError |
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except TypeError: |
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import streamlit as st |
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os.environ["FIREBASE_CREDENTIAL"] = st.secrets["FIREBASE_CREDENTIAL"] |
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cred = credentials.Certificate(json.loads(str(os.environ.get("FIREBASE_CREDENTIAL")), strict=False)) |
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if not firebase_admin._apps: |
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firebase_admin.initialize_app(cred, {'storageBucket': 'healthhack-store.appspot.com'}) |
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def get_csv(index_name, isDiseases = True): |
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while index_name[-1]=="/": |
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index_name = index_name[:-1] |
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dir = index_name.split("/") |
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for i in range(len(dir)): |
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path = '/'.join(dir[:i+1]) |
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if not os.path.exists(path): |
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os.mkdir(path) |
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if (not os.path.exists(index_name+"/diseases.csv") or |
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not os.path.exists(index_name+"/network.csv") |
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): |
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bucket = storage.bucket() |
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blob = bucket.blob(f"{index_name}/diseases.csv") |
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blob.download_to_filename(f"{index_name}/diseases.csv") |
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bucket = storage.bucket() |
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blob = bucket.blob(f"{index_name}/network.csv") |
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blob.download_to_filename(f"{index_name}/network.csv") |
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if isDiseases: |
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store = pd.read_csv(f"{index_name}/diseases.csv") |
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return store |
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else: |
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store = pd.read_csv(f"{index_name}/network.csv") |
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return store |
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def get_store(index_name, embeddings = None): |
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while index_name[-1]=="/": |
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index_name = index_name[:-1] |
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dir = index_name.split("/") |
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for i in range(len(dir)): |
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path = '/'.join(dir[:i+1]) |
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if not os.path.exists(path): |
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os.mkdir(path) |
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if (not os.path.exists(index_name+"/index.faiss") or |
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not os.path.exists(index_name+"/index.pkl") |
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): |
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bucket = storage.bucket() |
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blob = bucket.blob(f"{index_name}/index.pkl") |
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blob.download_to_filename(f"{index_name}/index.pkl") |
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bucket = storage.bucket() |
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blob = bucket.blob(f"{index_name}/index.faiss") |
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blob.download_to_filename(f"{index_name}/index.faiss") |
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if embeddings is None: |
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model_name = "bge-large-en-v1.5" |
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model_kwargs = {"device": "cpu"} |
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encode_kwargs = {"normalize_embeddings": True} |
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embeddings = HuggingFaceBgeEmbeddings( |
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model_kwargs = model_kwargs, |
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encode_kwargs = encode_kwargs) |
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store = FAISS.load_local(index_name, embeddings) |
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return store |
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def update_csv_from_local(index_name): |
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while index_name[-1]=="/": |
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index_name = index_name[:-1] |
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pathdir = index_name.split("/") |
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for i in range(len(pathdir)): |
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path = '/'.join(pathdir[:i+1]) |
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if not os.path.exists(path): |
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raise Exception("Index name does not exist locally") |
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if (not os.path.exists(index_name+"/diseases.csv") or |
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not os.path.exists(index_name+"/network.csv") |
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): |
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raise("Index is missing some files (network.csv, diseases.csv)") |
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bucket = storage.bucket() |
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blob = bucket.blob(index_name+"/diseases.csv") |
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blob.upload_from_filename(index_name+"/diseases.csv") |
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blob = bucket.blob(index_name+"/network.csv") |
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blob.upload_from_filename(index_name+"/network.csv") |
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return True |
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def update_store_from_local(index_name): |
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while index_name[-1]=="/": |
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index_name = index_name[:-1] |
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pathdir = index_name.split("/") |
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for i in range(len(pathdir)): |
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path = '/'.join(pathdir[:i+1]) |
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if not os.path.exists(path): |
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raise Exception("Index name does not exist locally") |
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if (not os.path.exists(index_name+"/index.faiss") or |
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not os.path.exists(index_name+"/index.pkl") |
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): |
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raise("Index is missing some files (index.faiss, index.pkl)") |
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bucket = storage.bucket() |
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blob = bucket.blob(index_name+"/index.faiss") |
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blob.upload_from_filename(index_name+"/index.faiss") |
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blob = bucket.blob(index_name+"/index.pkl") |
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blob.upload_from_filename(index_name+"/index.pkl") |
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return True |
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if __name__ == "__main__": |
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print("y r u running dis") |